In the last few years, energy efficiency has become a research field of high interest for governments and industry. In order to understand consumption data and provide useful information for high-level decision making processes in energy efficiency, there is the problem of information modelling and knowledge discovery coming from a set of energy consumption sensors. This paper focuses in this problem, and explores the use of symbolic regression techniques able to find out patterns in data that can be used to extract an analytical formula that explains the behaviour of energy consumption in a set of public buildings. More specifically, we test the feasibility of different representations such as trees and straight line programs for the implementation of genetic programming algorithms, to find out if a building consumption data can be suitably explained from the energy consumption data from other similar buildings. Our experimental study suggests that the Straight Line Programs representation may overcome the limitations of traditional tree-based representations and provides accurate patterns of energy consumption models.

This work addresses the problem of the recognition of human activities in Ambient Assisted Living (AAL) scenarios. The ultimate goal of a good AAL system is to learn and recognise behaviours or routines of the person or people living at home, in order to help them if something unusual happens. In this paper, we explore the advances in unobstrusive depth camera-based technologies to detect human activities involving motion. We explore the benefits of a framework for gesture recognition in this field, in contrast to raw signal processing techniques. For the framework validation, Hidden Markov Models and Dynamic Time Warping have been implemented for the action learning and recognition modules as a baseline due to their well known results in the field. The results obtained after the experimentation suggest that the depth sensors are accurate enough and useful in this field, and also that the preprocessing framework studied may result in a suitable methodology.

Dynamical recurrent neural networks are models suitable to solve problems where the input and output data may have dependencies in time, like grammatical inference or time series prediction. However, traditional training algorithms for these networks sometimes provide unsuitable results because of the vanishing gradient problems. This work focuses on hybrid proposals of training algorithms for this type of neural networks. The methods studied are based on the combination of heuristic procedures with gradient-based algorithms. In the experimental section, we show the advantages and disadvantages that we may find when using these training techniques in time series prediction problems, and provide a general discussion about the problems and cases of different hybridations based on genetic evolutionary algorithms.

Artificial Neural Networks are bioinspired mathematical models that have been widely used to solve many complex problems. However, the training of a Neural Network is a difficult task since the traditional training algorithms may get trapped into local solutions easily. This problem is greater in Recurrent Neural Networks, where the traditional training algorithms sometimes provide unsuitable solutions. Some evolutionary techniques have also been used to improve the training stage, and to overcome such local solutions, but they have the disadvantage that the time taken to train the network is high. The objective of this work is to show that the use of some non-linear programming techniques is a good choice to train a Neural Network, since they may provide suitable solutions quickly. In the experimental section, we apply the models proposed to train an Elman Recurrent Neural Network in real-life Time Series Prediction problems.

This paper presents the development of BioMen (Biological Management Executed over Network), an
Internet-managed system. By using service ontologies, the user is able to perform services remotely from a web browser. In addition, artificial intelligence techniques have been incorporated so that the necessary information may be obtained for the study of biodiversity. We have built a tool which will be of particular use to botanists and which can by accessed from anywhere in the world thanks to Internet technology. In this paper, we shall present the results and how we developed the tool.

In this paper, we study and predict the economic indebtedness for the autonomic of Spain. In turn, we use model of neural network. In this study, we assess the feasibility of the Time-Delay neural network as an alternative to these classical forecasting models. This neural network permits accumulate more values of pass and a best prediction of the future. We show the MSE assignment to check the good forecasting of indebtedness economic.

The application of Artificial Intelligence (AI) techniques to the problem of botanical identification is not particularly widespread even less so on Internet. There are several interactive identification systems but they usually deal with raw knowledge so it appears that “research and development of web-based expert systems are still in their early stage” (Li et al., 2002). In this paper we present the G.R.E.E.N. (Gymnosperms Remote Expert Executed over Network) system as an expert system for the identification of Iberian Gymnosperms which allows on-line uncertainty queries to be made. The system is operative and it can be consulted in http://drimys.ugr.es/experto/index.html.